Impara Automating Data Analysis Workflows | Advanced Analysis and Automation for Investors

Automation is a powerful tool for investors who want to maximize efficiency and accuracy in their analysis. By automating repetitive investment analysis tasks, you can save time, reduce manual errors, and ensure consistency across your workflow. Common tasks that benefit from automation include calculating returns and volatility for multiple assets, generating summary statistics, and producing regular reports on portfolio performance. Instead of performing these calculations manually for each asset or portfolio, automation enables you to handle large volumes of data quickly and with minimal intervention.


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import pandas as pd
import numpy as np

def calculate_return_and_volatility(price_df):
    """
    Calculate daily returns and annualized volatility for a DataFrame of prices.
    
    Args:
        price_df (pd.DataFrame): DataFrame where each column is an asset and rows are price data indexed by date.
        
    Returns:
        pd.DataFrame: DataFrame with columns 'Return' and 'Volatility' for each asset.
    """
    # Calculate daily returns
    returns = price_df.pct_change().dropna()
    # Calculate mean daily return
    mean_return = returns.mean()
    # Calculate annualized return (assuming 252 trading days)
    annual_return = mean_return * 252
    # Calculate annualized volatility
    volatility = returns.std() * np.sqrt(252)
    # Combine into a summary DataFrame
    summary = pd.DataFrame({
        'Return': annual_return,
        'Volatility': volatility
    })
    return summary

Using functions like calculate_return_and_volatility allows you to streamline your analysis, especially when dealing with multiple assets or portfolios. Instead of repeating code for each asset, you can define a function once and reuse it as needed. Loops in Python let you automate the process further by applying your function to several DataFrames, each representing a different set of asset prices or portfolios. This approach not only saves time but also reduces the risk of copy-paste errors and ensures your results are consistent and reproducible. By combining functions and loops, you can build efficient workflows that scale with your data and analytical needs.


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# Suppose you have price data for three different portfolios
portfolio1 = pd.DataFrame({'AAPL': [150, 152, 154, 153], 'MSFT': [300, 305, 310, 308]})
portfolio2 = pd.DataFrame({'GOOGL': [2700, 2720, 2735, 2740], 'AMZN': [3400, 3425, 3430, 3440]})
portfolio3 = pd.DataFrame({'TSLA': [700, 710, 720, 730], 'NFLX': [500, 510, 520, 530]})

portfolios = [portfolio1, portfolio2, portfolio3]
results = []

for idx, pf in enumerate(portfolios, 1):
    summary = calculate_return_and_volatility(pf)
    print(f"Portfolio {idx} metrics:\n{summary}\n")
    results.append(summary)

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Sezione 3. Capitolo 1

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Suggested prompts:

Can you explain how the annualized return and volatility are calculated in the function?

How can I modify the function to handle missing data in the price DataFrame?

Can you show how to visualize the results for each portfolio?

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import pandas as pd
import numpy as np

def calculate_return_and_volatility(price_df):
    """
    Calculate daily returns and annualized volatility for a DataFrame of prices.
    
    Args:
        price_df (pd.DataFrame): DataFrame where each column is an asset and rows are price data indexed by date.
        
    Returns:
        pd.DataFrame: DataFrame with columns 'Return' and 'Volatility' for each asset.
    """
    # Calculate daily returns
    returns = price_df.pct_change().dropna()
    # Calculate mean daily return
    mean_return = returns.mean()
    # Calculate annualized return (assuming 252 trading days)
    annual_return = mean_return * 252
    # Calculate annualized volatility
    volatility = returns.std() * np.sqrt(252)
    # Combine into a summary DataFrame
    summary = pd.DataFrame({
        'Return': annual_return,
        'Volatility': volatility
    })
    return summary


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# Suppose you have price data for three different portfolios
portfolio1 = pd.DataFrame({'AAPL': [150, 152, 154, 153], 'MSFT': [300, 305, 310, 308]})
portfolio2 = pd.DataFrame({'GOOGL': [2700, 2720, 2735, 2740], 'AMZN': [3400, 3425, 3430, 3440]})
portfolio3 = pd.DataFrame({'TSLA': [700, 710, 720, 730], 'NFLX': [500, 510, 520, 530]})

portfolios = [portfolio1, portfolio2, portfolio3]
results = []

for idx, pf in enumerate(portfolios, 1):
    summary = calculate_return_and_volatility(pf)
    print(f"Portfolio {idx} metrics:\n{summary}\n")
    results.append(summary)

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 3. Capitolo 1

Automating Data Analysis Workflows

1. What is the main benefit of automating analysis tasks for investors?

2. How can Python functions help reduce errors in repetitive calculations?

3. Which Python construct is used to repeat actions for multiple items?

Automating Data Analysis Workflows

1. What is the main benefit of automating analysis tasks for investors?

2. How can Python functions help reduce errors in repetitive calculations?

3. Which Python construct is used to repeat actions for multiple items?